Quantifier/Degree-modifier constructions

Vorbemerkung

Dieses Skript gehört zum Begleitmaterial zu dem Aufsatz “Doch Quantität vor Qualität? Motivationen und Mechanismen des Wandels in einer konstruktionalen Großfamilie deutscher Quantifizierer und Gradmodifizierer”. Ebenso wie der Aufsatz eine Folgestudie zu Neels et al. (2023) darstellt, baut das Skript auf den dazugehörigen Daten und Analysen auf. Der Datensatz wurde jedoch um zuvor nicht berücksichigte Belege für [bisschen zu X] ergänzt.

Vorbereitungen

Zunächst müssen einige Pakete geladen werden. Zwei davon, concordances und collostructions, sind nicht über CRAN verfügbar und können daher nicht über install.packages installiert werden. collostructions ist über Susanne Flachs Website verfügbar, concordances auf Github. Letzteres kann über devtools::install_github installiert werden (hierfür muss das Paket devtools installiert sein.)

# install concordances package (if not yet installed)
if(!is.element("concordances", installed.packages())) {
  devtools::install_github("hartmast/concordances")
}

# load packages
# install.packages("https://sfla.ch/wp-content/uploads/2021/02/collostructions_0.2.0.tar.gz", repos = NULL)
library(collostructions) # available at sfla.ch
library(concordances)
library(tidyverse)
library(data.table)
library(ggraph)
library(igraph)
library(networkD3)
library(DT)
library(readxl)
library(vroom)
library(corrplot)

Suchanfragen

Das Webkorpus DECOW wurde genutzt, um nach Quantifizierer-/Gradmodifikatorkonstruktionen zu suchen. Folgende Anfragen wurden verwendet:

# list of files
f <- list.files(pattern = "xml")

# get queries from concordance file:
sapply(1:length(f), function(i) trimws(gsub("<query>|</query>", "", readLines(f[i], n = 7)[6])))

##  [1] "[word=\"ein(e[nm])?\"] [word=\"[F]ünkchen\"] [tag=\"N.*|ADJ.*|V.*\"] 2948"        
##  [2] "[word=\"ein(e[nm])?\"] [word=\"[F]ünkchen\"] \"zu\" [] 10"                        
##  [3] "[word=\"[eE]in(e[nm])?\"] [word=\"[Qq]u[eä]ntchen\"] [tag=\"N.*|ADJ.*|V.*\"] 3423"
##  [4] "[word=\"[Ee]in(e[mn])?\"] [word=\"[Hh]auch\"] [tag=\"N.*|ADJ.*\"] 16716"          
##  [5] "[word=\"[Ee]in(e[mn])?\"] [word=\"[Hh]auch\"] \"zu\" [] 781"                      
##  [6] "[word=\"[eE]in(e[nm])?\"] [word=\"[Qq]u[eä]ntchen\"] \"zu\" [] 76"                
##  [7] "[word=\"[Ee]in(e[nm])?\"] [word=\"[ZTzt]acken\"] [tag=\"N.*|ADJ.*\"] 1830"        
##  [8] "[word=\"[Ee]in(e[nm])?\"] [word=\"[ZTzt]acken\"] \"zu\" [] 392"                   
##  [9] "[word=\"[Ee]in(e[mn])?\"] [word=\"[Tt]ick\"] [tag=\"ADJ.*|N.*\"] 17707"           
## [10] "[word=\"[Ee]in(e[nm])?\"] [word=\"[Tt]ick\"] [word=\"zu\"] [] 6032"               
## [11] "[word=\"[Ee]iner?\"] [word=\"[Hh]andvoll\"] [tag=\"N.*\"] 35998"                  
## [12] "[word=\"[Ee]iner?\"] [word=\"[Ii]dee\"] [tag=\"N.*|ADJ.*\"] 3900"                 
## [13] "[word=\"[Ee]iner?\"] [word=\"[Id]dee\"] \"zu\" [tag=\"ADJ.*\"] 349"               
## [14] "[word=\"[Ee]iner?\"] [word=\"[Ss]pur\"] [tag=\"N.*|ADJ.*\"] 11192"                
## [15] "[word=\"[Ee]iner?\"] [word=\"[Ss]pur\"] \"zu\" [tag=\"ADJ.*\"] 3442"

Daten einlesen

Die Daten werden mit Hilfe des Pakets concordances eingelesen.

# read data ---------------------------------------------------------------

fuenk <- getNSE("ein_em_Fuenkchen_ADJ_N.xml", xml = T, tags = T, context_tags = F, verbose = T)
fuenk_zu <- getNSE("ein_em_Fuenkchen_zu.xml", xml = T, tags = T, context_tags = F, verbose = T)
tack_zack <- getNSE("ein_enm_Tacken_Zacken_N_ADJ.xml", xml = T, context_tags = F)
tack_zack_zu <- getNSE("ein_enm_Tacken_Zacken_zu.xml", xml = T, context_tags = F)
handvoll <- getNSE("eine_r_Handvoll_ADJ_N.xml", xml = T, context_tags = F, tags = T)
idee <- getNSE("eine_r_Idee_ADJ_N.xml", xml = T, context_tags = F, tags = T)
idee_zu <- getNSE("eine_r_Idee_zu_ADJ.xml", xml = T, context_tags = F, tags = T)
tick <- getNSE("ein_enm_Tick_ADJ_N.xml", xml = T, context_tags = F, tags = T)
tick_zu <- getNSE("ein_enm_Tick_zu.xml", xml = T, context_tags = F, tags = T)
bisschen <- fread("ein_bisschen_adj_n_lemma_frequency_list.txt", col.names = c("Token", "Freq", "bla"))
bisschen_zu <- fread("ein_bisschen_zu_adj_frequency_list.txt", col.names = c("Token", "Freq", "bla"))
hauch <- getNSE("ein_enm_Hauch_ADJ_N.xml", xml = T, context_tags = F, tags = T)
hauch_zu <- getNSE("ein_enm_Hauch_zu.xml", xml = T, context_tags = F, tags = T)
spur <- getNSE("eine_r_Spur_N_Adj.xml", xml = T, context_tags = F, tags = T)
spur_zu <- getNSE("eine_r_Spur_zu_ADJ.xml", xml = T, context_tags = F, tags = T)
quaentchen <- getNSE("ein_emn_Quäentchen_N_ADJ_V.xml", xml = T, context_tags = F, tags = T)
quaentchen_zu <- getNSE("ein_enm_Quäentchen_zu.xml", xml = T, context_tags = F, tags = T)

Data Wrangling

Mit der folgenden Funktion werden Duplikate eliminiert. Außerdem werden die Konkordanzen für “ein(e) X ADJ/N” und “ein(e) X zu ADJ” kombiniert. Auch wird jeder Tabell eine Lemma-Spalte hinzugefügt, die sich auf die automatische Annotation stützt.

# function for removing duplicates -----------
remove_duplicates <- function(df) {
  
  x <- which(duplicated(df$Left) &
               duplicated(df$Key) &
               duplicated(df$Right))
  
  if(length(x) > 0) {
    df <- df[-x,]
  }
  
  return(df)
  
}

# remove "unknown" lemma from "bisschen" dataframe
bisschen <- bisschen[grep("(unknown)", bisschen$Token, invert = T),]
bisschen_zu <- bisschen_zu[grep("(unknown)", bisschen_zu$Token, invert = T),]

# get modified nouns and adjectives in
# "bisschen" dataframe
bisschen$Lemma <- last_left(bisschen$Token, n = 1)
bisschen_zu$Lemma <- last_left(bisschen_zu$Token, n = 1)

# remove empty column from bisschen
bisschen <- bisschen[,c(1,2,4)]
bisschen_zu <- bisschen_zu[,c(1,2,4)]

# backup copy
bisschen_backup <- bisschen
bisschen_zu_backup <- bisschen_zu

# some are duplicated, so we have to sum them up:
bisschen <- bisschen %>% group_by(Lemma) %>% summarise(
  Freq = sum(Freq)
)

bisschen_zu <- bisschen_zu %>% group_by(Lemma) %>% summarise(
  Freq = sum(Freq)
)

# remove duplicates
idee <- remove_duplicates(idee)
tick <- remove_duplicates(tick)
handvoll <- remove_duplicates(handvoll)
tack_zack <- remove_duplicates(tack_zack)
fuenk <- remove_duplicates(fuenk)
hauch <- remove_duplicates(hauch)
spur <- remove_duplicates(spur)
idee_zu <- remove_duplicates(idee_zu)
tick_zu <- remove_duplicates(tick_zu)
tack_zack_zu <- remove_duplicates(tack_zack_zu)
fuenk_zu <- remove_duplicates(fuenk_zu)
hauch_zu <- remove_duplicates(hauch_zu)
spur_zu <- remove_duplicates(spur_zu)
quaentchen <- remove_duplicates(quaentchen)
quaentchen_zu <- remove_duplicates(quaentchen_zu)

# combine "zu" and "normal" ones:
idee <- rbind(mutate(idee), cxn_type = "ADJ_N",
      mutate(idee_zu), cxn_type = "zu_ADJ")
spur <- rbind(mutate(spur), cxn_type = "ADJ_N",
              mutate(spur_zu), cxn_type = "zu_ADJ")
fuenk <- rbind(mutate(fuenk), cxn_type = "ADJ_N",
              mutate(fuenk_zu), cxn_type = "zu_ADJ")
spur <- rbind(mutate(spur), cxn_type = "ADJ_N",
              mutate(spur_zu), cxn_type = "zu_ADJ")
tack_zack <- rbind(mutate(tack_zack), cxn_type = "ADJ_N",
              mutate(tack_zack_zu), cxn_type = "zu_ADJ")
tick <- rbind(mutate(tick), cxn_type = "ADJ_N",
              mutate(tick_zu), cxn_type = "zu_ADJ")
hauch <- rbind(mutate(hauch), cxn_type = "ADJ_N",
              mutate(hauch_zu), cxn_type = "zu_ADJ")
quaentchen <- rbind(mutate(quaentchen), cxn_type = "ADJ_N",
                    mutate(quaentchen_zu), cxn_type = "zu_ADJ")
bisschen <- rbind(mutate(bisschen), cxn_type = "ADJ_N",
                  mutate(bisschen_zu), cxn_type = "zu_ADJ")

# add lemma column
idee$Lemma <- last_left(idee, Tag3_Key, 1)
tick$Lemma <- last_left(tick, Tag3_Key, 1)
fuenk$Lemma <- last_left(fuenk, Tag3_Key, 1)
tack_zack$Lemma <- last_left(tack_zack, Tag3_Key, 1)
handvoll$Lemma <- last_left(handvoll, Tag3_Key, 1)
spur$Lemma <- last_left(spur, Tag3_Key, 1)
hauch$Lemma <- last_left(hauch, Tag3_Key, 1)
quaentchen$Lemma <- last_left(quaentchen, Tag3_Key, 1)

Da sich vor allem bei Idee, aber auch bei Hauch und Spur noch viele Fehltreffer finden, wurden die entsprechenden Datensätze exportiert und anschließend manuell bereinigt.

# write_csv(idee, "idee_for_anno.csv")

# Hauch: add last_left of keyword
# hauch$Key_modified <- last_left(hauch$Key, n = 1, omit_punctuation = FALSE)

# spur$Key_modified <- last_left(spur$Key, n = 1, omit_punctuation = FALSE)

# write_csv(hauch, "hauch_for_anno.csv")
# write_csv(spur, "spur_for_anno.csv")

Hier werden die annotierten Datensätze wieder eingelesen und Fehltreffer entfernt:

# import data
idee <- read_xlsx("idee_for_anno.xlsx")
hauch <- read_xlsx("hauch_for_anno.xlsx")
spur <- read_xlsx("spur_for_anno.xlsx")

# remove false hits
idee <- filter(idee, keep == "y")
hauch <- filter(hauch, Modifier == "y")
spur <- filter(spur, Modifier == "y")

Im folgenden Abschnitt wird ein großer Dataframe erstellt, der alle Belege zusammen mit Informationen über ihre jeweilige Quelle erhält; die Informationen über die Quelle wurden der DECOW-Dokumentenliste entnommen.

# combine all:
d_all <- rbind(select(fuenk, c("Metatag1", "Left", "Key", "Right")),
      select(handvoll, c("Metatag1", "Left", "Key", "Right")),
      select(hauch, c("Metatag1", "Left", "Key", "Right")),
      select(idee, c("Metatag1", "Left", "Key", "Right")),
      select(quaentchen, c("Metatag1", "Left", "Key", "Right")),
      select(spur, c("Metatag1", "Left", "Key", "Right")),
      select(tack_zack, c("Metatag1", "Left", "Key", "Right")),
      select(tick, c("Metatag1", "Left", "Key", "Right")))

# list of DECOW documents
decowdoc <- vroom("/Volumes/My Passport/DECOW16BX-Corex/decow16b.doc.csv.gz", col_names = paste0("V", c(1:85)))

# only keep relevant columns
decowdoc <- decowdoc[,c(1:4)]

# join with d_all
d_all <- left_join(d_all, decowdoc, by = c("Metatag1" = "V4"))

# export
# write_excel_csv(d_all, "d_all.csv")

# re-import
# d_all <- read_csv("d_all.csv")

Die vollständige Liste ist hier verfügbar.

Da eine Durchsicht der Daten ergeben hat, dass es sich in den allermeisten Fällen, in denen ein Verb modifiziert wird, um Fehltreffer handelt, werden sie von der weiteren Analyse ausgeschlossen.

fuenk <- fuenk[grep("^V.*", last_left(fuenk$Tag2_Key, n = 1), invert = T),]
hauch <- hauch[grep("^V.*", last_left(hauch$Tag2_Key, n = 1), invert = T),]
tick <- tick[grep("^V.*", last_left(tick$Tag2_Key, n = 1), invert = T),]
quaentchen <- quaentchen[grep("^V.*", last_left(quaentchen$Tag2_Key, n = 1), invert = T),]
tack_zack <- tack_zack[grep("^V.*", last_left(tack_zack$Tag2_Key, n = 1), invert = T),]
tick <- tick[grep("^V.*", last_left(tick$Tag2_Key, n = 1), invert = T),]

Überblicksstatistik

Wie oft treten die einzelnen Konstruktionen mit Nomen, Adjektiven etc. auf?

# function for getting the distribution:
get_distro <- function(vec) {
  x <- gsub("(?<=.).*", "", last_left(trimws(vec), n = 1), perl = T) %>% table
  y <- x[which(names(x) %in% c("A", "N", "V"))]
  y <- c(y, "other" = sum(x[which(!names(x) %in% c("A", "N", "V"))]))
  return(y)
}

# function for finding comparatives:
get_compar <- function(df) {
  # find comparatives
  find_comparatives <- which(grepl("ADJ.*", last_left(df$Tag2_Key, n = 1)) &
grepl("er(e|es|en)?$", trimws(df$Key)))
  
  # add to df
  df$comparative <- sapply(1:nrow(df), function(i) ifelse(i %in% find_comparatives, "yes", "no"))
  
  return(table(df$comparative))
  
  
}

# get "zu ADJ"
get_zu <- function(df) {
  return(length(which(sapply(1:nrow(df), function(i) unlist(strsplit(df$Key[i], " "))[3])=="zu")))
}


# get POS distributions
get_distro(fuenk$Tag2_Key) %>% as.data.frame %>% t()

##     A    N other
## . 156 2674     3

distro <- bind_rows(
  get_distro(fuenk$Tag2_Key),
get_distro(handvoll$Tag2_Key),
get_distro(idee$Tag2_Key),
get_distro(hauch$Tag2_Key),
get_distro(quaentchen$Tag2_Key),
get_distro(spur$Tag2_Key),
get_distro(tack_zack[grepl("Tacken", tack_zack$Key, ignore.case = T),]$Tag2_Key),
get_distro(tack_zack[grepl("Zacken", tack_zack$Key, ignore.case = T),]$Tag2_Key),
get_distro(tick$Tag2_Key)
) %>% as_tibble %>% mutate(Cxn = c("Fünkchen", "Handvoll", "Idee", "Hauch", "Quäntchen", "Spur", "Tacken", "Zacken", "Tick")) %>% replace_na(list(A = 0, N = 0, V = 0))

# get comparative distributions
distro <- mutate(distro, comparatives = c(
  get_compar(fuenk)[2],
get_compar(handvoll)[2],
get_compar(idee)[2],
get_compar(hauch)[2],
get_compar(quaentchen)[2],
get_compar(spur)[2],
get_compar(tack_zack[grepl("Tacken", tack_zack$Key, ignore.case = T),])[2],
get_compar(tack_zack[grepl("Zacken", tack_zack$Key, ignore.case = T),])[2],
get_compar(tick)[2]
)) %>% replace_na(list(comparatives = 0))


# zu...
distro <- mutate(distro, zu = c(
  get_zu(fuenk),
get_zu(handvoll),
get_zu(idee),
get_zu(hauch),
get_zu(quaentchen),
get_zu(spur),
get_zu(tack_zack[grepl("Tacken", tack_zack$Key, ignore.case = T),]),
get_zu(tack_zack[grepl("Zacken", tack_zack$Key, ignore.case = T),]),
get_zu(tick)
))

# column with comparatives and "zu" in ADJ column
distro$ADJ <- paste0(distro$A, " (", distro$comparatives, "/", distro$zu, ")")
distro <- rename(distro, c("ADJ (comparative / excessive)" = "ADJ"))

# add column with sum total
distro$sum <- distro$A + distro$N  + distro$other

# reorder columns
distro[,c(4,2,6,3,6,5,7,8)] %>% datatable()

# POS distribution of "bisschen" -----

b_dist <- fread("ein_bisschen_adj_n_POS_frequency_list.txt", col.names = c("POS", "Freq", "bla"))

## Warning in fread("ein_bisschen_adj_n_POS_frequency_list.txt", col.names =
## c("POS", : Detected 1 column names but the data has 3 columns (i.e. invalid
## file). Added 2 extra default column names at the end.

# get pos:
b_dist$pos <- last_left(b_dist, POS, n = 1)

# coarse-grained POS
b_dist$pos1 <- ifelse(b_dist$pos %in% c("NE", "NN"), "N", "ADJ")

# tabulate
b_dist %>% group_by(pos1) %>% summarise(
  Freq = sum(Freq)
)

We use the list of lemmas attested in the concordances to extract their total frequency in the DECOW corpus from the DECOW lemma frequency list.

# list of all lemmas across dfs
lemmas_all <- c(idee$Lemma, tick$Lemma, fuenk$Lemma, tack_zack$Lemma,
  handvoll$Lemma, bisschen$Lemma, spur$Lemma, hauch$Lemma, 
  quaentchen$Lemma) %>% unique


# collostructional analyses -----------------------------------------------
# 
# read DECOW lemma frequencies
decow <- fread("/Volumes/TOSHIBA EXT/DECOW ngrams/decow16bx.lp.tsv")

# only keep verbs, nouns and adjectives
decow01 <- decow[V2 %in% c("NN", "ADJD", "ADJA", "VAINF", "VVFIN", "VVINF", "VAPP", "VVPP", "VVIZU", "VAIMP")]
colnames(decow01) <- c("lemma", "pos", "Freq")

# count POS
pos_tbl <- decow01 %>% group_by(pos) %>% summarise(
  Freq = sum(Freq)
)

# only keep lemmas attested in the constructions
decow <- decow01[lemma %in% lemmas_all]

# export 
# saveRDS(decow, "decow_modifier_lemmas.Rds")
#saveRDS(pos_tbl, "pos_tbl.Rds")

# re-import
decow <- readRDS("decow_modifier_lemmas.Rds")
pos_tbl <- readRDS("pos_tbl.Rds")

Some of the lemmas in the decow dataframe occur more than once (e.g. because they have multiple POS tags), so we have to sum them up first. Also, the idee dataframe still contains many false hits, so we limit it to its most frequent domain by far, comparatives.

# sum up frequencies of lemmas occuring more than once
decow_sum <- decow %>% group_by(lemma) %>% summarise(
  Freq = sum(Freq)
)

Collostructional analysis

We have to do some more data wrangling in order to create the input dataframes for collostructional analysis.

# frequency tables for the different constructions
idee_tbl <- idee %>% select(Lemma) %>% table %>% as.data.frame
fuenk_tbl <- fuenk %>% select(Lemma) %>% table %>% as.data.frame
handvoll_tbl <- handvoll %>%  select(Lemma) %>% table %>% as.data.frame
tick_tbl <- tick %>%  select(Lemma) %>% table %>% as.data.frame
tack_tbl <- tack_zack[grepl("Tacken", tack_zack$Key, ignore.case = T),] %>% 
  select(Lemma) %>% table %>% as.data.frame
zack_tbl <- tack_zack[grepl("Zacken", tack_zack$Key, ignore.case = T),] %>% 
  select(Lemma) %>% table %>% as.data.frame
hauch_tbl <- hauch %>%  select(Lemma) %>% table %>% as.data.frame
spur_tbl <- spur %>%  select(Lemma) %>% table %>% as.data.frame
quaentchen_tbl <- quaentchen %>% select(Lemma) %>% table %>% as.data.frame()

colnames(idee_tbl) <- colnames(fuenk_tbl) <- 
  colnames(handvoll_tbl) <- colnames(tack_tbl) <- 
  colnames(zack_tbl) <- colnames(tick_tbl) <-  
  colnames(spur_tbl) <- colnames(hauch_tbl) <-
  colnames(quaentchen_tbl) <- 
  c("lemma", "Freq_mod")

bisschen_tbl <- bisschen
colnames(bisschen_tbl) <- c("lemma", "Freq_bisschen")

# join dataframes
idee_tbl <- left_join(idee_tbl, decow_sum)
fuenk_tbl <- left_join(fuenk_tbl, decow_sum)
handvoll_tbl <- left_join(handvoll_tbl, decow_sum)
tack_tbl <- left_join(tack_tbl, decow_sum)
tick_tbl <- left_join(tick_tbl, decow_sum)
zack_tbl <- left_join(zack_tbl, decow_sum)
spur_tbl <- left_join(spur_tbl, decow_sum)
hauch_tbl <- left_join(hauch_tbl, decow_sum)
quaentchen_tbl <- left_join(quaentchen_tbl, decow_sum)
bisschen_tbl <- left_join(bisschen_tbl, decow_sum)

# replace NAs by 0
idee_tbl <- replace_na(idee_tbl, list(Freq_mod = 0, Freq = 0))
fuenk_tbl <- replace_na(fuenk_tbl, list(Freq_mod = 0, Freq = 0))
handvoll_tbl <- replace_na(handvoll_tbl, list(Freq_mod = 0, Freq = 0))
tack_tbl <- replace_na(tack_tbl, list(Freq_mod = 0, Freq = 0))
tick_tbl <- replace_na(tick_tbl, list(Freq_mod = 0, Freq = 0))
zack_tbl <- replace_na(zack_tbl, list(Freq_mod = 0, Freq = 0))
hauch_tbl <- replace_na(hauch_tbl, list(Freq_mod = 0, Freq = 0))
spur_tbl <- replace_na(spur_tbl, list(Freq_mod = 0, Freq = 0))
quaentchen_tbl <- replace_na(quaentchen_tbl, list(Freq_mod = 0, Freq = 0))
bisschen_tbl <- replace_na(bisschen_tbl, list(Freq_bisschen = 0, Freq = 0))

# reomove cases where cxn frequency is bigger than
# corpus frequency
idee_tbl <- idee_tbl[which(idee_tbl$Freq_mod <= idee_tbl$Freq),]
fuenk_tbl <- fuenk_tbl[which(fuenk_tbl$Freq_mod <= fuenk_tbl$Freq),]
handvoll_tbl <- handvoll_tbl[which(handvoll_tbl$Freq_mod <= handvoll_tbl$Freq),]
tack_tbl <- tack_tbl[which(tack_tbl$Freq_mod <= tack_tbl$Freq),]
tick_tbl <- tick_tbl[which(tick_tbl$Freq_mod <= tick_tbl$Freq),]
zack_tbl <- zack_tbl[which(zack_tbl$Freq_mod <= zack_tbl$Freq),]
spur_tbl <- spur_tbl[which(spur_tbl$Freq_mod <= spur_tbl$Freq),]
hauch_tbl <- hauch_tbl[which(hauch_tbl$Freq_mod <= hauch_tbl$Freq),]
quaentchen_tbl <- quaentchen_tbl[which(quaentchen_tbl$Freq_mod <= quaentchen_tbl$Freq),]
bisschen_tbl <- bisschen_tbl[which(bisschen_tbl$Freq_bisschen <= bisschen_tbl$Freq),]


# collexeme analysis ------------------------------------------------------

col_idee <- collex(idee_tbl,
       corpsize = 
         sum(pos_tbl[grep("ADJ.*", pos_tbl$pos),]$Freq))# %>%  write_excel_csv("idee_collex.csv")


col_fuenk <- collex(fuenk_tbl,
       corpsize = sum(pos_tbl$Freq)) # %>% write_excel_csv("fuenkchen_collex.csv")

col_handvoll <- collex(handvoll_tbl,
       corpsize = sum(pos_tbl$Freq)) # %>% write_csv("handvoll_collex.csv")

col_tack <- collex(tack_tbl, 
       corpsize = sum(pos_tbl$Freq)) # %>% write_csv("tack_collex.csv")

col_tick <- collex(tick_tbl, 
                   corpsize = sum(pos_tbl$Freq)) # %>% write_csv("tick_collex.csv")

col_zack <- collex(zack_tbl, 
       corpsize = sum(pos_tbl$Freq)) # %>% write_csv("zack_collex.csv")

col_spur <- collex(spur_tbl, 
                   corpsize = sum(pos_tbl$Freq)) # %>% write_csv("spur_collex.csv")


col_hauch <- collex(hauch_tbl, 
                   corpsize = sum(pos_tbl$Freq)) # %>% write_csv("hauch_collex.csv")

col_quaentchen <- collex(quaentchen_tbl, 
                    corpsize = sum(pos_tbl$Freq)) # %>% write_csv("quaentchen_collex.csv")

bisschen_tbl$Freq_bisschen <- as.numeric(bisschen_tbl$Freq_bisschen)

# omit items in which observed frequency in cxn is 
# larger than corpus frequency
bisschen_tbl1 <- bisschen_tbl[-which(bisschen_tbl$Freq_bisschen > bisschen_tbl$Freq),]

col_bisschen <- collex(as.data.frame(bisschen_tbl1), 
                   corpsize = sum(pos_tbl$Freq)) # %>% write_csv("bisschen_collex.csv")

Collostructional analysis: Results

Here are the results of the collostructional analyses (in alphabetical order).

ein bisschen

ein Hauch

eine Spur

ein Zacken

ein Tick

ein Tacken

eine Handvoll

ein Fünkchen

eine Idee

ein Quäntchen

Network analysis

The collexeme analysis is complemented by a network analysis. The aim of this analysis is to check whether different modified items combine with the modifiers to a similar degree or whether the items combining with the individual modifiers occupy certain semantic niches.

# first links, then edges

links <- rbind(
  col_idee %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "eine Idee") ,
  col_handvoll %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "eine Handvoll") ,
  col_fuenk %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "ein Fünkchen") ,
  col_tack %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "ein Tacken"),
  col_tick %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "ein Tick"),
  col_zack %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "ein Zacken"),
  col_hauch %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "ein Hauch"),
  col_spur %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "eine Spur"),
  col_quaentchen %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "ein Quäntchen"),
  col_bisschen %>% select(COLLEX, COLL.STR.LOGL) %>% mutate(LEX = "ein bisschen") ) %>%
  mutate(edge_type = LEX) %>%
  group_by(LEX) %>%
  slice(1:100) %>%
  ungroup()

# reorder columns
links <- links[,c(3,1,2,4)] %>% 
  arrange(edge_type)

# create dataframes for links and nodes
nodes_LEX = data.frame(links$LEX) %>%
  distinct() %>%
  rename(name = links.LEX) %>%
  mutate(node_type = name) %>%
  mutate(node_size = 10) %>%
  mutate(text_size = 100) %>%
  mutate(text_fontface = "bold") %>%
  mutate(shape = "circle") %>%
  mutate(label = name) 
nodes_COLLEX = data.frame(links$COLLEX) %>%
  distinct() %>%
  rename(name = links.COLLEX) %>%
  mutate(node_type = "COLLEX") %>%
  mutate(node_size = 1.5) %>%
  mutate(text_size = 1) %>%
  mutate(text_fontface = "plain") %>%
  mutate(label = NA) 
nodes_all = bind_rows(nodes_LEX, nodes_COLLEX) %>% 
  arrange(node_type)

# plot
col_graph <- graph_from_data_frame(links, nodes_all, directed = F)

set.seed(1995)
# used "kk" layout because it is less spread out
ggraph(col_graph, layout = "kk") +
  geom_edge_link(aes(color = edge_type), show.legend = FALSE,
                 end_cap = circle(.07, 'inches')) +
  scale_edge_color_manual(values = c("#FF0000", "#A7D547", "#FFA500", "#00FFFF", 
                                     "#FF00FF", "#00BFFF", "#008000",  "#CDAD5A", "#00FF00", "#AD7A44")) +
  geom_node_point(aes(color = node_type, size = node_size), show.legend = FALSE) +
  scale_color_manual(values = c("#000000", "#FF0000", "#A7D547", "#FFA500", "#00FFFF", 
                                "#FF00FF", "#00BFFF", "#008000",  "#CDAD5A", "#00FF00", "#AD7A44")) +
  geom_node_text(aes(label = label, size = text_size, fontface = text_fontface), vjust = 1, hjust = 1, show.legend = FALSE) +
  theme_void()

## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Removed 716 rows containing missing values (`geom_text()`).

# decreased width and height so the font size would come out as bigger
# ggsave("network_modifiers_100_kk.png", width = 15, height = 10)


# plot only with selected nodes

# select modifiers
# reorder columns
links2 <- links %>% filter(LEX %in% c("ein bisschen", "ein Tick", 
                                      "eine Idee", "ein Quäntchen") & 
                             edge_type %in% c("ein bisschen", "ein Tick", 
                                             "eine Idee", "ein Quäntchen"))

# create dataframes for links and nodes
nodes_LEX2 = data.frame(links2$LEX) %>%
  distinct() %>%
  rename(name = links2.LEX) %>%
  mutate(node_type = name) %>%
  mutate(node_size = 2) %>%
  mutate(text_size = 4) %>%
  mutate(text_fontface = "bold") 
nodes_COLLEX2 = data.frame(links2$COLLEX) %>%
  distinct() %>%
  rename(name = links2.COLLEX) %>%
  mutate(node_type = "COLLEX") %>%
  mutate(node_size = 1.5) %>%
  mutate(text_size = 2.5) %>%
  mutate(text_fontface = "plain") 
nodes_all2 <- bind_rows(nodes_LEX2, nodes_COLLEX2) %>% 
  arrange(node_type)

# plot
col_graph2 <- graph_from_data_frame(links2, nodes_all2, directed = F)


# plot with labels ----------------------------------------

modifiers = c("eine Idee", "eine Handvoll", "ein Fünkchen", "ein Tacken", "ein Tick", "ein Zacken",
              "ein Hauch", "eine Spur", "ein Quäntchen", "ein bisschen")

# plot with layout "kk"
ggraph(col_graph, layout = "kk") +
  geom_edge_link(aes(color = edge_type), show.legend = FALSE,
                 end_cap = circle(.07, 'inches')) +
  scale_edge_color_manual(values = c("#FF0000", "#A7D547", "#FFA500", "#00FFFF", 
                                     "#FF00FF", "#00BFFF", "#008000",  "#CDAD5A", "#00FF00", "#AD7A44")) +
  geom_node_point(aes(color = node_type, size = node_size), show.legend = FALSE) +
  scale_color_manual(values = c("#000000", "#FF0000", "#A7D547", "#FFA500", "#00FFFF", 
                                "#FF00FF", "#00BFFF", "#008000",  "#CDAD5A", "#00FF00", "#AD7A44")) +
  geom_node_text(aes(label = name, size = text_size, fontface = text_fontface), vjust = 1, hjust = 1, show.legend = FALSE) +
  theme_void()

# ggsave("network_modifiers_kk.png", width = 40, height = 20)

Number of shared collexemes

While the collexeme analyses give an impression of the semantics of each construction, another interesting question is how many of the collexemes are shared between the individual constructions. In order to assess this question, we create a word-construction-matrix and visualize it using a heatmap.

# create long list of lemmas and cxns
lemmas_df <- rbind(
  mutate(select(fuenk, Lemma), cxn = "Fünkchen"),
  mutate(select(hauch, Lemma), cxn = "Hauch"),
  mutate(select(handvoll, Lemma), cxn = "Handvoll"),
  mutate(select(idee, Lemma), cxn = "Idee"),
  mutate(select(quaentchen, Lemma), cxn = "Quäntchen"),
  mutate(select(spur, Lemma), cxn = "Spur"),
  mutate(select(tack_zack[grepl("Tacken", tack_zack$Key, ignore.case = T),], Lemma), cxn = "Tacken"),
  mutate(select(tack_zack[grepl("Zacken", tack_zack$Key, ignore.case = T),], Lemma), cxn = "Zacken"),
  mutate(select(tick, Lemma), cxn = "Tick"),
  mutate(select(bisschen, Lemma), cxn = "bisschen")
)

# find out how many items are shared between each 2 cxns
cxns <- unique(lemmas_df$cxn)

# empty dataframe
mydf <- data.frame(matrix(NA, nrow = 10, ncol = 10))
colnames(mydf) <- cxns
rownames(mydf) <- cxns

for(i in 1:length(cxns)) {
  for(j in 1:length(cxns)) {
    mydf[j,i] <- length(intersect(unique(filter(lemmas_df, cxn == colnames(mydf)[i])$Lemma),
          unique(filter(lemmas_df, cxn == rownames(mydf)[j])$Lemma)))
    
  }
}


# change to long dataframe
mydf2 <- rownames_to_column(mydf)
mydf_long <- pivot_longer(mydf2, 2:length(mydf2))
colnames(mydf_long) <- c("cxn_A", "cxn_B", "n")


# add columns "shared" and "non-shared" to visualize
mydf_long$shared <-sapply(1:nrow(mydf_long), function(i) length(intersect(unique(lemmas_df[which(lemmas_df$cxn==mydf_long$cxn_A[i]),]$Lemma),
unique(lemmas_df[which(lemmas_df$cxn==mydf_long$cxn_B[i]),]$Lemma)))
)

mydf_long$nonshared <- sapply(1:nrow(mydf_long), function(i) length(setdiff(unique(lemmas_df[which(lemmas_df$cxn==mydf_long$cxn_A[i]),]$Lemma),
unique(lemmas_df[which(lemmas_df$cxn==mydf_long$cxn_B[i]),]$Lemma)))
)

# select relevant columns
mydf_long2 <- mydf_long %>% select(cxn_A, cxn_B, shared, nonshared)

# add relative column
# relative to sum of shared and nonshared items:
mydf_long2$rel <- mydf_long2$shared / (mydf_long2$shared + mydf_long2$nonshared)

# absolute values with shared and sum total of items
mydf_long2$abs <- paste(mydf_long2$shared, "\n(", mydf_long2$nonshared + mydf_long2$shared, ")", sep = "")

# even better: paste it to x axis labels
mydf_long2$x <- paste(mydf_long2$cxn_A, " (", mydf_long2$nonshared + mydf_long2$shared, ")", sep="")

# heatmap
mydf_long2 %>% filter(rel < 1) %>% ggplot(aes(x = cxn_B, y = x, fill = rel, label = shared)) + geom_tile() + geom_text() + scale_fill_gradient(low = "yellow", high = "darkred") +
  xlab("Construction") + ylab("Construction") + 
  guides(fill = "none") + theme_classic() + theme(axis.text.x = element_text(angle=45, hjust=.9)) +     scale_y_discrete(limits=rev)

# ggsave("heatmap002_all_types.png", width = 7, height = 6)

# incl. shared with itself:
mydf_long2 %>% ggplot(aes(x = cxn_B, y = x, fill = rel, label = shared)) + geom_tile() + geom_text() + scale_fill_gradient(low = "yellow", high = "darkred") +
  xlab("Construction") + ylab("Construction") + 
  guides(fill = "none") + theme_classic() + theme(axis.text.x = element_text(angle=45, hjust=.9)) +     scale_y_discrete(limits=rev)

The same limited to the top 100 collexemes for each construction:

# top 100 collexemes:

top100_collexemes <- tibble(
  "Fünkchen" = col_fuenk %>% select(COLLEX) %>% head(100) %>% unname,
  "Handvoll" = col_handvoll %>% select(COLLEX) %>% head(100) %>% unname,
  "Hauch" =  col_hauch %>% select(COLLEX) %>% head(100) %>% unname,
  "Idee" = col_idee %>% select(COLLEX) %>% head(100) %>% unname,
  "Quäntchen" = col_quaentchen %>% select(COLLEX) %>% head(100) %>% unname,
  "Spur" = col_spur %>% select(COLLEX) %>% head(100) %>% unname,
  "Tacken" = col_tack %>% select(COLLEX) %>% head(100) %>% unname,
  "Tick" = col_tick %>% select(COLLEX) %>% head(100) %>% unname,
  "Zacken" = col_zack %>% select(COLLEX) %>% head(100) %>% unname,
  "bisschen" = col_bisschen %>% select(COLLEX) %>% head(100) %>% unname
)

# remove "$" from column names
colnames(top100_collexemes) <- gsub("[[:punct:]]", "", colnames(top100_collexemes))


# lemmas_df only with top 100 lemmas
# create long list of lemmas and cxns


lemmas_df <- rbind(
  mutate(select(filter(fuenk, Lemma %in% unlist(top100_collexemes$`Fünkchen`)), Lemma), cxn = "Fünkchen"),
  mutate(select(filter(hauch, Lemma %in% unlist(top100_collexemes$Hauch)), Lemma), cxn = "Hauch"),
  mutate(select(filter(handvoll, Lemma %in% unlist(top100_collexemes$Handvoll)), Lemma), cxn = "Handvoll"),
  mutate(select(filter(idee, Lemma %in% unlist(top100_collexemes$Idee)), Lemma), cxn = "Idee"),
  mutate(select(filter(quaentchen, Lemma %in% unlist(top100_collexemes$`Quäntchen`)), Lemma), cxn = "Quäntchen"),
  mutate(select(filter(spur, Lemma %in% unlist(top100_collexemes$Spur)), Lemma), cxn = "Spur"),
  mutate(select(filter(tack_zack[grepl("Tacken", tack_zack$Key, ignore.case = T),], Lemma %in% unlist(top100_collexemes$Tacken)), Lemma), cxn = "Tacken"),
  mutate(select(filter(tack_zack[grepl("Zacken", tack_zack$Key, ignore.case = T),], Lemma %in% unlist(top100_collexemes$Zacken)), Lemma), cxn = "Zacken"),
  mutate(select(filter(tick, Lemma %in% unlist(top100_collexemes$Tick)), Lemma), cxn = "Tick"),
  mutate(select(filter(bisschen, Lemma %in% unlist(top100_collexemes$bisschen)), Lemma), cxn = "bisschen")
)


# The rest is largely copy&pasted from above as it remains almost unchanged:

# empty dataframe
mydf <- data.frame(matrix(NA, nrow = 10, ncol = 10))
colnames(mydf) <- cxns
rownames(mydf) <- cxns

for(i in 1:length(cxns)) {
  for(j in 1:length(cxns)) {
    mydf[j,i] <- length(intersect(filter(lemmas_df, cxn == colnames(mydf)[i])$Lemma,
          filter(lemmas_df, cxn == rownames(mydf)[j])$Lemma))
    
  }
}


# change to long dataframe
mydf2 <- rownames_to_column(mydf)
mydf_long <- pivot_longer(mydf2, 2:length(mydf2))
colnames(mydf_long) <- c("cxn_A", "cxn_B", "n")

# get a more reliable impression of the number of shared items by dividing the frequency value by the total number of types of the less frequent construction

mytypes <- lemmas_df %>% group_by(cxn) %>% summarise(
  types = length(unique(Lemma))
)

# add n of types for cxn A and B
mydf_long <- left_join(mydf_long, mytypes, by = c("cxn_A" = "cxn"))
mydf_long <- rename(mydf_long, types_A = types)
mydf_long <- left_join(mydf_long, mytypes, by = c("cxn_B" = "cxn"))
mydf_long <- rename(mydf_long, types_B = types)

# get smaller n (average not needed here because all are 100)
mydf_long$n_min_types <- ifelse(mydf_long$types_A < mydf_long$types_B, mydf_long$types_A, mydf_long$types_B)

# "relative" frequency
mydf_long$rel <- mydf_long$n / mydf_long$n_min_types


# heatmap
mydf_long %>% filter(rel < 1) %>% ggplot(aes(x = cxn_A, y = cxn_B, fill = rel, label = n)) + geom_tile() + geom_text() + scale_fill_gradient(low = "yellow", high = "darkred") +
  xlab("Construction") + ylab("Construction") + guides(fill = 'none') + theme_classic() + theme(axis.text.x = element_text(angle=45, hjust=.9)) + scale_y_discrete(limits=rev)

mydf_long %>% filter(rel < 1) %>% ggplot(aes(x = cxn_A, y = cxn_B, fill = n, label = n)) + geom_tile() + geom_text() + scale_fill_gradient(low = "yellow", high = "darkred") +
  xlab("Construction") + ylab("Construction") + guides(fill = 'none') + theme_classic() + theme(axis.text.x = element_text(angle=45, hjust=.9)) +
  scale_y_discrete(limits=rev)

References

• Flach, Susanne. 2017. collostructions: An R Implementation for the Family of Collostructional Methods. www.bit.ly/sflach.

• Schäfer, Roland. 2015. Processing and querying large corpora with the COW14 architecture. In Piotr Bański, Hanno Biber, Evelyn Breiteneder, Marc Kupietz, Harald Lüngen & Andreas Witt (eds.), Challenges in the Management of Large Corpora (CMLC-3), 28–34.

• Schäfer, Roland & Felix Bildhauer. 2012. Building Large Corpora from the Web Using a New Efficient Tool Chain. In Nicoletta Calzolari, Khalid Choukri, Terry Declerck, Mehmet Uğur Doğan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk & Stelios Piperidis (eds.), Proceedings of LREC 2012, 486–493.